{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "img (InputLayer)                [(None, 32, 32, 3)]  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d (Conv2D)                 (None, 30, 30, 32)   896         img[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, 28, 28, 64)   18496       conv2d[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D)    (None, 9, 9, 64)     0           conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)               (None, 9, 9, 64)     36928       max_pooling2d[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_3 (Conv2D)               (None, 9, 9, 64)     36928       conv2d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add (Add)                       (None, 9, 9, 64)     0           conv2d_3[0][0]                   \n",
      "                                                                 max_pooling2d[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_4 (Conv2D)               (None, 9, 9, 64)     36928       add[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_5 (Conv2D)               (None, 9, 9, 64)     36928       conv2d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_1 (Add)                     (None, 9, 9, 64)     0           conv2d_5[0][0]                   \n",
      "                                                                 add[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_6 (Conv2D)               (None, 7, 7, 64)     36928       add_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling2d (Globa (None, 64)           0           conv2d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dense (Dense)                   (None, 256)          16640       global_average_pooling2d[0][0]   \n",
      "__________________________________________________________________________________________________\n",
      "dropout (Dropout)               (None, 256)          0           dense[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 10)           2570        dropout[0][0]                    \n",
      "==================================================================================================\n",
      "Total params: 223,242\n",
      "Trainable params: 223,242\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ResNet 模型\n",
    "# 使用CIFAR10建立一个ResNet模型\n",
    "inputs1 = keras.Input(shape=(32,32,3), name=\"img\")\n",
    "x = layers.Conv2D(32, 3, activation=\"relu\")(inputs1)\n",
    "x = layers.Conv2D(64, 3, activation=\"relu\")(x)\n",
    "block_1_output = layers.MaxPooling2D(3)(x)\n",
    "\n",
    "x2 = layers.Conv2D(64, 3, activation=\"relu\", padding=\"same\")(block_1_output)\n",
    "x2 = layers.Conv2D(64, 3, activation=\"relu\", padding=\"same\")(x2)\n",
    "block_2_output = layers.add([x2, block_1_output])\n",
    "\n",
    "x3 = layers.Conv2D(64, 3, activation=\"relu\", padding=\"same\")(block_2_output)\n",
    "x3 = layers.Conv2D(64, 3, activation=\"relu\", padding=\"same\")(x3)\n",
    "block_3_output = layers.add([x3, block_2_output])\n",
    "\n",
    "x4 = layers.Conv2D(64, 3, activation=\"relu\")(block_3_output)\n",
    "x4 = layers.GlobalAveragePooling2D()(x4)\n",
    "x4 = layers.Dense(256, activation=\"relu\")(x4)\n",
    "x4 = layers.Dropout(0.5)(x4)\n",
    "outputs1 = layers.Dense(10)(x4)\n",
    "\n",
    "model1 = keras.Model(inputs1, outputs1)\n",
    "\n",
    "\n",
    "# 绘制模型可视化结构图\n",
    "keras.utils.plot_model(model1, \"resnet-cifar.png\", show_shapes=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50000, 32, 32, 3)\n",
      "(50000, 1)\n",
      "(10000, 32, 32, 3)\n",
      "(10000, 1)\n"
     ]
    }
   ],
   "source": [
    "# 加载数据\n",
    "(x_train, y_train), (x_test, y_test) = keras.datasets.cifar10.load_data()\n",
    "print(x_train.shape)\n",
    "print(y_train.shape)\n",
    "print(x_test.shape)\n",
    "print(y_test.shape)\n",
    "\n",
    "# 归一化\n",
    "x_train = x_train.astype(\"float32\")/255.0\n",
    "x_test = x_test.astype(\"float32\")/255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 40000 samples, validate on 10000 samples\n",
      "Epoch 1/15\n",
      "40000/40000 [==============================] - 100s 3ms/sample - loss: 1.8887 - acc: 0.2833 - val_loss: 1.5876 - val_acc: 0.4083\n",
      "Epoch 2/15\n",
      "40000/40000 [==============================] - 99s 2ms/sample - loss: 1.4746 - acc: 0.4583 - val_loss: 1.3834 - val_acc: 0.4938\n",
      "Epoch 3/15\n",
      "40000/40000 [==============================] - 99s 2ms/sample - loss: 1.2573 - acc: 0.5470 - val_loss: 1.1206 - val_acc: 0.5919\n",
      "Epoch 4/15\n",
      "40000/40000 [==============================] - 100s 3ms/sample - loss: 1.1055 - acc: 0.6085 - val_loss: 1.0094 - val_acc: 0.6327\n",
      "Epoch 5/15\n",
      "40000/40000 [==============================] - 99s 2ms/sample - loss: 0.9757 - acc: 0.6557 - val_loss: 0.8969 - val_acc: 0.6853\n",
      "Epoch 6/15\n",
      "40000/40000 [==============================] - 100s 2ms/sample - loss: 0.8826 - acc: 0.6890 - val_loss: 0.7655 - val_acc: 0.7303\n",
      "Epoch 7/15\n",
      "40000/40000 [==============================] - 98s 2ms/sample - loss: 0.8055 - acc: 0.7199 - val_loss: 0.7464 - val_acc: 0.7358\n",
      "Epoch 8/15\n",
      "40000/40000 [==============================] - 97s 2ms/sample - loss: 0.7424 - acc: 0.7441 - val_loss: 0.8783 - val_acc: 0.6981\n",
      "Epoch 9/15\n",
      "40000/40000 [==============================] - 98s 2ms/sample - loss: 0.6864 - acc: 0.7633 - val_loss: 0.7445 - val_acc: 0.7430\n",
      "Epoch 10/15\n",
      "40000/40000 [==============================] - 98s 2ms/sample - loss: 0.6417 - acc: 0.7803 - val_loss: 0.6869 - val_acc: 0.7680\n",
      "Epoch 11/15\n",
      "40000/40000 [==============================] - 98s 2ms/sample - loss: 0.6057 - acc: 0.7949 - val_loss: 0.7814 - val_acc: 0.7511\n",
      "Epoch 12/15\n",
      "40000/40000 [==============================] - 104s 3ms/sample - loss: 0.5690 - acc: 0.8069 - val_loss: 0.9182 - val_acc: 0.7279\n",
      "Epoch 13/15\n",
      "22784/40000 [================>.............] - ETA: 40s - loss: 0.5338 - acc: 0.8193"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-4-f5381e83c563>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;31m# 将数据集限制在前5000个样本中，限制时间\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m \u001b[0mmodel1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m64\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m15\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalidation_split\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     10\u001b[0m \u001b[1;31m# model1.fit(x_train[:10000], y_train[:10000], batch_size=64, epochs=10, validation_split=0.2)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32me:\\programdata\\anaconda3\\envs\\tf2\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)\u001b[0m\n\u001b[0;32m    726\u001b[0m         \u001b[0mmax_queue_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmax_queue_size\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    727\u001b[0m         \u001b[0mworkers\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mworkers\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 728\u001b[1;33m         use_multiprocessing=use_multiprocessing)\n\u001b[0m\u001b[0;32m    729\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    730\u001b[0m   def evaluate(self,\n",
      "\u001b[1;32me:\\programdata\\anaconda3\\envs\\tf2\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training_v2.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, model, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, **kwargs)\u001b[0m\n\u001b[0;32m    322\u001b[0m                 \u001b[0mmode\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mModeKeys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTRAIN\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    323\u001b[0m                 \u001b[0mtraining_context\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtraining_context\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 324\u001b[1;33m                 total_epochs=epochs)\n\u001b[0m\u001b[0;32m    325\u001b[0m             \u001b[0mcbks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmake_logs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepoch_logs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtraining_result\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mModeKeys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTRAIN\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    326\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32me:\\programdata\\anaconda3\\envs\\tf2\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training_v2.py\u001b[0m in \u001b[0;36mrun_one_epoch\u001b[1;34m(model, iterator, execution_function, dataset_size, batch_size, strategy, steps_per_epoch, num_samples, mode, training_context, total_epochs)\u001b[0m\n\u001b[0;32m    121\u001b[0m         step=step, mode=mode, size=current_batch_size) as batch_logs:\n\u001b[0;32m    122\u001b[0m       \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 123\u001b[1;33m         \u001b[0mbatch_outs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mexecution_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    124\u001b[0m       \u001b[1;32mexcept\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mStopIteration\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mOutOfRangeError\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    125\u001b[0m         \u001b[1;31m# TODO(kaftan): File bug about tf function and errors.OutOfRangeError?\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32me:\\programdata\\anaconda3\\envs\\tf2\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training_v2_utils.py\u001b[0m in \u001b[0;36mexecution_function\u001b[1;34m(input_fn)\u001b[0m\n\u001b[0;32m     84\u001b[0m     \u001b[1;31m# `numpy` translates Tensors to values in Eager mode.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     85\u001b[0m     return nest.map_structure(_non_none_constant_value,\n\u001b[1;32m---> 86\u001b[1;33m                               distributed_function(input_fn))\n\u001b[0m\u001b[0;32m     87\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     88\u001b[0m   \u001b[1;32mreturn\u001b[0m \u001b[0mexecution_function\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32me:\\programdata\\anaconda3\\envs\\tf2\\lib\\site-packages\\tensorflow_core\\python\\eager\\def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    455\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    456\u001b[0m     \u001b[0mtracing_count\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_tracing_count\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 457\u001b[1;33m     \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    458\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mtracing_count\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_tracing_count\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    459\u001b[0m       \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call_counter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcalled_without_tracing\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32me:\\programdata\\anaconda3\\envs\\tf2\\lib\\site-packages\\tensorflow_core\\python\\eager\\def_function.py\u001b[0m in \u001b[0;36m_call\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    485\u001b[0m       \u001b[1;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    486\u001b[0m       \u001b[1;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 487\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# pylint: disable=not-callable\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    488\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    489\u001b[0m       \u001b[1;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32me:\\programdata\\anaconda3\\envs\\tf2\\lib\\site-packages\\tensorflow_core\\python\\eager\\function.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1821\u001b[0m     \u001b[1;34m\"\"\"Calls a graph function specialized to the inputs.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1822\u001b[0m     \u001b[0mgraph_function\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_define_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1823\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_filtered_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# pylint: disable=protected-access\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1824\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1825\u001b[0m   \u001b[1;33m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32me:\\programdata\\anaconda3\\envs\\tf2\\lib\\site-packages\\tensorflow_core\\python\\eager\\function.py\u001b[0m in \u001b[0;36m_filtered_call\u001b[1;34m(self, args, kwargs)\u001b[0m\n\u001b[0;32m   1139\u001b[0m          if isinstance(t, (ops.Tensor,\n\u001b[0;32m   1140\u001b[0m                            resource_variable_ops.BaseResourceVariable))),\n\u001b[1;32m-> 1141\u001b[1;33m         self.captured_inputs)\n\u001b[0m\u001b[0;32m   1142\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1143\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0m_call_flat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcaptured_inputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcancellation_manager\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32me:\\programdata\\anaconda3\\envs\\tf2\\lib\\site-packages\\tensorflow_core\\python\\eager\\function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[1;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[0;32m   1222\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mexecuting_eagerly\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1223\u001b[0m       flat_outputs = forward_function.call(\n\u001b[1;32m-> 1224\u001b[1;33m           ctx, args, cancellation_manager=cancellation_manager)\n\u001b[0m\u001b[0;32m   1225\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1226\u001b[0m       \u001b[0mgradient_name\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_delayed_rewrite_functions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mregister\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32me:\\programdata\\anaconda3\\envs\\tf2\\lib\\site-packages\\tensorflow_core\\python\\eager\\function.py\u001b[0m in \u001b[0;36mcall\u001b[1;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[0;32m    509\u001b[0m               \u001b[0minputs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    510\u001b[0m               \u001b[0mattrs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"executor_type\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexecutor_type\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"config_proto\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 511\u001b[1;33m               ctx=ctx)\n\u001b[0m\u001b[0;32m    512\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    513\u001b[0m           outputs = execute.execute_with_cancellation(\n",
      "\u001b[1;32me:\\programdata\\anaconda3\\envs\\tf2\\lib\\site-packages\\tensorflow_core\\python\\eager\\execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[0;32m     59\u001b[0m     tensors = pywrap_tensorflow.TFE_Py_Execute(ctx._handle, device_name,\n\u001b[0;32m     60\u001b[0m                                                \u001b[0mop_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mattrs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 61\u001b[1;33m                                                num_outputs)\n\u001b[0m\u001b[0;32m     62\u001b[0m   \u001b[1;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     63\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "\n",
    "y_train = keras.utils.to_categorical(y_train, 10)\n",
    "y_test = keras.utils.to_categorical(y_test, 10)\n",
    "\n",
    "model1.compile(optimizer=keras.optimizers.RMSprop(1e-3),\n",
    "              loss=keras.losses.CategoricalCrossentropy(from_logits=True),\n",
    "              metrics=[\"acc\"])\n",
    "\n",
    "# 开始训练\n",
    "history = model1.fit(x_train, y_train, batch_size=64, epochs=15, validation_split=0.2)\n",
    "# model1.fit(x_train[:10000], y_train[:10000], batch_size=64, epochs=10, validation_split=0.2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 查看模型结构\n",
    "model1.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From e:\\programdata\\anaconda3\\envs\\tf2\\lib\\site-packages\\tensorflow_core\\python\\ops\\resource_variable_ops.py:1781: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "If using Keras pass *_constraint arguments to layers.\n",
      "INFO:tensorflow:Assets written to: resnet_model\\assets\n"
     ]
    }
   ],
   "source": [
    "# 保存模型\n",
    "model1.save(\"resnet_model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'history' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-7-05f4e2fac465>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# 绘制模型准确率和损失函数曲线图\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0macc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhistory\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'acc'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mval_acc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhistory\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'val_acc'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mloss\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhistory\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'loss'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'history' is not defined"
     ]
    }
   ],
   "source": [
    "# 绘制模型准确率和损失函数曲线图\n",
    "acc = history.history['acc']\n",
    "val_acc = history.history['val_acc']\n",
    "\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.plot(acc, label='Training Accuracy')\n",
    "plt.plot(val_acc, label='Validation Accuracy')\n",
    "plt.legend(loc='lower right')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.ylim([min(plt.ylim()),1])\n",
    "plt.title('Training and Validation Accuracy')\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.plot(loss, label='Training Loss')\n",
    "plt.plot(val_loss, label='Validation Loss')\n",
    "plt.legend(loc='upper right')\n",
    "plt.ylabel('Cross Entropy')\n",
    "plt.title([0, 1.0])\n",
    "plt.title('Training and Validation Loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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